class_docstr: Proposes new trials using a linear model.
  
  The model suggests an architecture no deeper than those in the trials.
  Sometimes (by a coin flip) adds one layer of depth to the suggestion.
class_docstr_tok: ["Proposes","new","trials","using","a","linear","model",".","The","model","suggests","an","architecture","no","deeper","than","those","in","the","trials",".","Sometimes","(","by","a","coin","flip",")","adds","one","layer","of","depth","to","the","suggestion","."]
docstr: Args: phoenix_spec: phoenix_spec_pb2 for the experiment.
docstr_tok: ["Args",":","phoenix_spec",":","phoenix_spec_pb2","for","the","experiment","."]
code: def __init__(self, phoenix_spec):
      """Args: phoenix_spec: phoenix_spec_pb2 for the experiment."""
      self._phoenix_spec = phoenix_spec
      self._block_indices = np.unique(
          [block_builder.BlockType.EMPTY_BLOCK.value]
          + [idx.value for idx in common.block_indices(phoenix_spec)]
      )
  
file_path: model_search/model_search/search/linear_model.py
imports: from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.proto import phoenix_spec_pb2
  from model_search.search import common
  from model_search.search import search_algorithm
  import numpy
  import sklearn
  import sklearn.linear_model
label_desc: Use image node_id 1 to create a new LinearModel object from inherited base classes: search_algorithm with example: obj = LinearModel(phoenix_spec)